Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Naif Almusallam^1,*, Junaid Qayyum^2,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3653-3687, 2025, DOI:10.32604/cmc.2025.067220 - 23 September 2025

Abstract This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection, unsupervised clustering, and ensemble learning to improve classification performance in financial transaction monitoring systems. The framework is structured into three core layers: (1) feature selection using Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Mutual Information (MI) to reduce dimensionality and enhance input relevance; (2) anomaly detection through unsupervised clustering using K-Means, Density-Based Spatial Clustering (DBSCAN), and Hierarchical Clustering to flag suspicious patterns in unlabeled data; and (3) final classification using a voting-based hybrid ensemble of Support Vector Machine (SVM),… More >

Irbek Morgoev¹, Roman Klyuev^2,*, Angelika Morgoeva¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025

Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

Ananya Jha¹, Amaresh Jha^2,*

Journal of Cyber Security, Vol.7, pp. 109-134, 2025, DOI:10.32604/jcs.2025.064429 - 30 May 2025

Abstract This paper explores the methodologies employed in the study of mobile and social media phishing, aiming to enhance the understanding of these evolving threats and develop robust countermeasures. By synthesizing existing research, we identify key approaches, including surveys, controlled experiments, data mining, and machine learning, to gather and analyze data on phishing tactics. These methods enable us to uncover patterns in attacker behavior, pinpoint vulnerabilities in mobile and social platforms, and evaluate the effectiveness of current detection and prevention strategies. Our findings highlight the growing sophistication of phishing techniques, such as social engineering and deceptive More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*, Yahia Said³, Shaaban M. Shaaban³, Husam Lahza⁴, Aws I. AbuEid⁵, Abdulrahman Alzahrani⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3181-3212, 2025, DOI:10.32604/cmes.2025.061608 - 03 March 2025

Abstract Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and… More >

Yuanyuan Ma¹, Ang Chen¹, Cunzhi Hou¹, Ruixia Jin², Jinghui Zhang¹, Ruixiang Li^3,4,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3301-3323, 2024, DOI:10.32604/cmc.2024.056854 - 18 November 2024

Abstract Currently, telecom fraud is expanding from the traditional telephone network to the Internet, and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights. However, existing telecom fraud identification methods based on blacklists, reputation, content and behavioral characteristics have good identification performance in the telephone network, but it is difficult to apply to the Internet where IP (Internet Protocol) addresses change dynamically. To address this issue, we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering (DC-FIPD). First, we… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051 - 30 January 2024

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network… More >

Ayesha Aslam¹, Adil Hussain^2,*

Journal on Artificial Intelligence, Vol.6, pp. 1-21, 2024, DOI:10.32604/jai.2024.047226 - 31 January 2024

Abstract With the increased accessibility of global trade information, transaction fraud has become a major worry in global banking and commerce security. The incidence and magnitude of transaction fraud are increasing daily, resulting in significant financial losses for both customers and financial professionals. With improvements in data mining and machine learning in computer science, the capacity to detect transaction fraud is becoming increasingly attainable. The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud. The research looks at the efficacy of these machine learning… More >

Xuanchen Zhou^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4073-4087, 2023, DOI:10.32604/cmc.2023.040253 - 26 December 2023

Abstract With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly,… More >

Yih Bing Chu^*, Zhi Min Lim, Bryan Keane, Ping Hao Kong, Ahmed Rafat Elkilany, Osama Hisham Abusetta

Journal of Cyber Security, Vol.5, pp. 33-46, 2023, DOI:10.32604/jcs.2023.045422 - 03 November 2023

Abstract The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud, particularly in credit card transactions. Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising from potentially fraudulent activities. However, a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations. While sampling techniques can significantly reduce computational time, the resulting dataset relies on generated data and the accuracy of the pre-processing… More >